Caclium pulsing analysis

First-pass attempt at characterizing calcium spikes

# install.packages("Rcatch22")
# install.packages("dichromat")
# install.packages("DescTools")

library(Rcatch22)
library(smooth)

Loading required package: greybox
Package "greybox", v2.0.0 loaded.

This is package "smooth", v4.0.0

library(tidyverse)

── Attaching core tidyverse packages ────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ lubridate::hm() masks greybox::hm()
✖ dplyr::lag()    masks stats::lag()
✖ tidyr::spread() masks greybox::spread()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors

source("traceAnalysisFxns.r")

numRuns <- function(x, threshold=0.2,len=2) 
{
    x[x>=threshold]
    out <- rle(x)$lengths
    length(out[out>=len])
}

numSpikes <- function(x, threshold=0.17)
{
    length(which(x>=threshold))
}
avgSpikeLength <- function(x, threshold=0.17) 
{
    pts_above_th <- x[x>=threshold]
    out <- tryCatch({rle(pts_above_th)$lengths},error=function(cond){NA})
    tryCatch({mean(out)},error=function(cond){message(cond);NA})
}

avgTimeBetweenSpikes <- function(x, threshold=0.17)
{
    pts_above_th <- x[x>=threshold]
    out <- tryCatch({inverse.rle(pts_above_th)$lengths},error=function(cond){NA})
    tryCatch({mean(out)},error=function(cond){NA})
}


std_colnames <- c("reg_id","im_idx","time_sec","fura_ratio","expt_date")

fixColnames <- function(x) sapply(x, function(z) 
    switch(z,
           ND.T="im_idx",
           "ROI ID" = "reg_id",
           totalSec = "time_sec",
           Fura_Ratio = "fura_ratio",
           ratio_340_380 = "fura_ratio",
           "Ratio 340/380" = "fura_ratio",
           ROI = "reg_id",
           z))

convertTime <- function(dat) {
    cn <- colnames(dat)
    if("Time_ms" %in% cn & ! "time_sec" %in% cn) {
        dat <- dat %>% separate(Time_ms, c("Minute", "Second"), sep = ":", convert = TRUE) %>% 
            mutate(time_sec = Minute*60 + Second)  %>% select(all_of(setdiff(c(cn,"time_sec"),c("Time_ms","Minute","Second"))))
    } else {
        dat
    }
}

processRawXLSX <- function(fpath, filter_min_area=100) {
    stopifnot(any(grepl("\\.xlsx", fpath)))
    
    expt_date <- substr(sapply(strsplit(fpath, "/"), "[[", 7),1,8)
    cond <- NA

    if(any(grepl("csv", fpath))) cond_temp <- sapply(strsplit(sapply(strsplit(fpath, "/"), "[[", 7), "\\.csv\\?"), "[[", 1)
    if(any(grepl("xlsx", fpath))) cond_temp <- sapply(strsplit(sapply(strsplit(fpath, "/"), "[[", 7), "\\.xlsx\\?"), "[[", 1)

    cond_pt1 <- sapply(strsplit(cond_temp, "_"), "[[", 2)
    cond_pt2 <- sapply(strsplit(cond_temp, "_"), "[[", 3)
    cond_pt3 <- ifelse(length(strsplit(cond_temp, "_")) >= 4, sapply(strsplit(cond_temp, "_"), "[[", 4), "")
    cond_temp <- paste(cond_pt1, cond_pt2, cond_pt3, sep="_")

    if(any(grepl("naive", cond_temp))) {
        naive_temp <- c(cond_pt1,cond_pt2,cond_pt3)[grep("naive",c(cond_pt1,cond_pt2,cond_pt3))]
        cond <- paste(expt_date, naive_temp, sep="_")
    }
    if(any(grepl("untreated", cond_temp))) 
        cond <- paste(expt_date, "naive", sep="_")
    if(any(grepl("PLX",cond_temp))) 
        cond <- paste(expt_date, sapply(strsplit(cond_temp, "_"), "[[", 3), sep="_")
    if(any(grepl("free",cond_temp))) 
        cond <- paste(expt_date, "3day8uMPLXCa2free", sep="_")
    if(any(grepl("Ca2containing",cond_temp))) 
        cond <- paste(expt_date, "3day8uMPLX_w_Ca2", sep="_")
    if(any(grepl("MEK",cond_temp))) 
        cond <- paste(expt_date, "30minMEKi", sep="_")

    if(any(grepl("dropbox",fpath))) {
        temp <- tempfile()
        download.file(fpath, temp, mode="wb")
    } else {
        temp <- fpath
    }
    dat <- readxl::read_xlsx(temp)
    keep_ocols <- c("Time [m:s]","ND.T","ROI ID","Intensity(340)","StDev(340)","Intensity(380)","StDev(380)","ROI Area [µm²]","Ratio 340/380")
    dat <- dat[,keep_ocols]
    times <- unique(dat$`Time [m:s]`)
    diff_times <- difftime(times, times[1], units="secs", tz="UTC")
    dat$time_sec <- DescTools::RoundTo(diff_times[match(dat$`Time [m:s]`,times)], multiple=5)
    # remove objects < 100 µM^2
    dat <- dat[dat$`ROI Area [µm²]` > filter_min_area,]
    
    # fix colnames
    colnames(dat) <- fixColnames(colnames(dat))
    dat$expt_date <- expt_date
    dat$cond <- cond
    return(as.data.frame(dat[,c(std_colnames,"cond")]))
}

preprocessData <- function(fpath) {
    expt_date <- substr(sapply(strsplit(fpath, "/"), "[[", 7),1,8)

    cond_temp <- sapply(strsplit(sapply(strsplit(fpath, "/"), "[[", 7), "\\.csv\\?"), "[[", 1)
    cond_pt1 <- sapply(strsplit(cond_temp, "_"), "[[", 2)
    cond_pt2 <- sapply(strsplit(cond_temp, "_"), "[[", 3)
    cond_pt3 <- sapply(strsplit(cond_temp, "_"), "[[", 4)
    cond_temp <- paste(cond_pt1, cond_pt2, cond_pt3, sep="_")

    if(any(grepl("naive", cond_temp))) 
        cond <- paste(expt_date, "naive", sep="_")
    if(any(grepl("untreated", cond_temp))) 
        cond <- paste(expt_date, "naive", sep="_")
    if(any(grepl("PLX",cond_temp))) 
        cond <- paste(expt_date, sapply(strsplit(cond_temp, "_"), "[[", 3), sep="_")
    if(any(grepl("free",cond_temp))) 
        cond <- paste(expt_date, "8day8uMPLXCa2free", sep="_")
    if(any(grepl("Ca2containing",cond_temp))) 
        cond <- paste(expt_date, "3day8uMPLX_w_Ca2", sep="_")
    if(any(grepl("MEK",cond_temp))) 
        cond <- paste(expt_date, "30minMEKi", sep="_")
    

    d <- read.csv(fpath)
    d <- convertTime(d)
    colnames(d) <- fixColnames(colnames(d))
    d <- d[,intersect(std_colnames,colnames(d))]
    d$expt_date <- expt_date
    d$cond <- cond
    return(d)
}

sampleROIs <- function(condition, ids=rois, n=100) sample(ids[grep(condition,ids)],n)

plotPCcatch <- function(roi, group_ids=clust$cluster, ...) {
    groups <- unique(group_ids)
    mycolors <- dichromat::colorschemes$Categorical.12[seq_along(groups)]
    plot(pc_catch[['x']][,1], pc_catch[['x']][,2], type="n",
         xlab="PC1", ylab="PC2", ...)
    sapply(groups, function(group) points(pc_catch[['x']][which(group_ids==group & rois %in% roi),1], 
                                          pc_catch[['x']][which(group_ids==group & rois %in% roi),2], 
                                          col=mycolors[match(group,groups)],
                                          pch=19, cex=0.5,))
    return(invisible(NULL))
}

Datasets

20220425 = naive
20230426 = untreated
20211114 = tolerant? 3 days?
20220321_fura2_A375H2bRFP_4day8uMPLX 20220321_fura2_A375H2bRFP_5day8uMPLX 20230426_idlingCa2free_pulsing002_V02

fpaths <- c("https://www.dropbox.com/scl/fi/yprt6h4s2khr7sk0kaoav/20230426_untreated_pulsing_mod.csv?rlkey=bt575yvb612cgf1bi23fb9n99&dl=1",
            # "https://www.dropbox.com/scl/fi/q5gxqbib7sjkbnzrjsl2q/20220425_Fura2_naive_pulsing_mod.csv?rlkey=9frhbyh7urxdm8s6h1k8n5vut&dl=1",
            "https://www.dropbox.com/scl/fi/fk0fh1trr46aromfqd31k/20220321_fura2_A375H2bRFP_5day8uMPLX.csv?rlkey=jhhm821t8q63c3n6yb6udasxg&dl=1",
            "https://www.dropbox.com/scl/fi/r0bitvu4ljz19jah9f9p8/20220321_fura2_A375H2bRFP_7day8uMPLX.csv?rlkey=i6wnyo48z5a0v4ovsrgysz2xm&dl=1",
            "https://www.dropbox.com/scl/fi/9j6dvk9vnar3cn5xe24he/20220321_fura2_A375H2bRFP_10day8uMPLX.csv?rlkey=62l5qpih4oetwrte0k3bk3szr&dl=1",
             "https://www.dropbox.com/scl/fi/6pq4d62ser0bcsuvwfcpv/20230426_idlingCa2free_pulsing002_V02_mod.csv?rlkey=k19wxifmfh3rn69zdgn0kb3tc&dl=1"
            )

rawdatapaths <- c(
                  "https://www.dropbox.com/scl/fi/f0ap9tuwfldtrjk2pd3yn/20220425_Fura2_naive002.xlsx?rlkey=dx1zwj6ov8zwzqt944jwc05z9&dl=1",
                  "https://www.dropbox.com/scl/fi/ru1cunsuqgi691ua05dgs/20220425_Fura2_naive003.xlsx?rlkey=f1q7uh454sqjspmr3anabdy1k&dl=1"
                  )

fp <- "https://www.dropbox.com/s/elwck3cnpzvg4uf/20211114_A375_Ca2%2B_pulsing_data.csv?dl=1"

d <- read.csv(fp)
colnames(d) <- c("reg_id", "fura_ratio", "time_sec", "time_min")
d$expt_date <- "20211114"
d$im_idx <- match(d$time_sec, sort(unique(d$time_sec)))
colnames(d) <- fixColnames(colnames(d))
d <- d[,intersect(std_colnames,colnames(d))]
d$cond <- "20211114_14day8uMPLX"

d <- rbind(d, do.call(rbind, lapply(fpaths, preprocessData)))
d <- rbind(d, do.call(rbind, lapply(rawdatapaths, processRawXLSX)))

trying URL 'https://www.dropbox.com/scl/fi/f0ap9tuwfldtrjk2pd3yn/20220425_Fura2_naive002.xlsx?rlkey=dx1zwj6ov8zwzqt944jwc05z9&dl=1'
Content type 'application/binary' length 18257512 bytes (17.4 MB)
==================================================
downloaded 17.4 MB

trying URL 'https://www.dropbox.com/scl/fi/ru1cunsuqgi691ua05dgs/20220425_Fura2_naive003.xlsx?rlkey=f1q7uh454sqjspmr3anabdy1k&dl=1'
Content type 'application/binary' length 16680046 bytes (15.9 MB)
==================================================
downloaded 15.9 MB

# calculate/adjust columns
d$reg_id <- paste(d$cond, d$reg_id, sep="_")
d$intensity_mean <- d$fura_ratio

# ensure all times are a multiple of 5
d$time_sec <- DescTools::RoundTo(d$time_sec, multiple=5)

NOTE

Experiments are each of different duration. To facilitate interpretation, use number of time points of the shortest dataset for each

expt_dates <- unique(d$expt_date)
conditions <- unique(d$cond)

n_im_idx <- min(sapply(conditions, function(co) length(unique((d[d$cond==co,"im_idx"])))))

temp <- lapply(unique(d$cond), function(co) {
    a <- d[d$cond==co,]
    a <- a[a$time_sec %in% tail(unique(a$time_sec), n_im_idx),]
    a$time_sec <- DescTools::RoundTo(a$time_sec - min(a$time_sec), multiple=5)
    # a$time_sec <- seq(0,((n_im_idx-1)*5),5)
    a$im_idx <- match(a$time_sec, sort(unique(a$time_sec)))
    rownames(a) <- NULL
    return(a)
})

d <- do.call(rbind, temp)

rois <- unique(d$reg_id)
times <- unique(d$time_sec)

# fr = fura2_ratio
fr <- data.frame(matrix(d$fura_ratio, ncol=length(rois)))
colnames(fr) <- as.character(rois)

# linear model - intersect
l <- do.call(c, lapply(conditions, function(co) {
    apply(fr[,grepl(co,colnames(fr))], 2, function(x) coef(lm(x ~ times))[2])
}))


# mean fura_ratio
fr_mean <- do.call(cbind,lapply(conditions, function(co) {
    apply(fr[,grepl(co,colnames(fr))], 1, mean)
}))
colnames(fr_mean) <- conditions

# frn = fura2_ratio normalized (mean-centered)
frn <- do.call(cbind, lapply(conditions, function(co) {
    apply(fr[,grepl(co,colnames(fr))], 2, function(x) x - fr_mean[,match(co,conditions)])
}))

d$fura_ratio_norm <- as.vector(frn)
d$int_mean_norm <- as.vector(frn)

Things to consider

• DTW
  
• Sum of time above threshold
  
• Stretches of times above threshold
• Times between spikes
• Number of spikes
• Determine the background for each trace

interesting ROIs: 11, 12, 18, 20, 25, 27

checkthese <- paste("20211114_14day8uMPLX", c(11, 12, 18, 20, 25, 27), sep="_")
plot(fura_ratio ~ time_sec, data=d[d$reg_id %in% checkthese,], type="n", ylim=c(0,1))
for(roi in checkthese) lines(fura_ratio ~ time_sec, data=d[d$reg_id==roi,], col=dichromat::colorschemes$Categorical.12[match(roi,checkthese)])

spike_height <- sd(d$fura_ratio_norm)*2.5
roi_list <- lapply(unique(d$reg_id), function(roi) 
    {
        dat <- d[d$reg_id==roi,"fura_ratio_norm"]
        thresh <- .15
        out <- data.frame(reg_id=roi,
                          mean=mean(dat),
                          sd=sd(dat),
                          avg.spike.length=avgSpikeLength(dat, threshold=thresh),
                          num.runs=numRuns(dat, threshold=thresh),
                          num.spikes=numSpikes(dat,threshold=thresh),
                          slope=coef(lm(dat ~ times))[2],
                          intercept=coef(lm(dat ~ times))[1]
                          )
        rownames(out) <- NULL
        return(out)
    })

sumstats <- do.call(rbind, roi_list)
sumstats$avg.spike.length[is.na(sumstats$avg.spike.length)] <- 0

catch <- lapply(unique(d$reg_id), function(roi) 
    {
        dat <- catch22_all(d[d$reg_id==roi,"fura_ratio_norm"])
        dat <- as.data.frame(dat)
        rownames(dat) <- dat[,1]
        dat$names <- NULL
        dat <- t(dat)

        return(dat)
    })

Warning: As of 0.1.14 the feature 'CO_f1ecac' returns a double instead of int

pc_catch <- prcomp(scale(catch))
plot(pc_catch[['x']][,1], pc_catch[['x']][,2], pch=19, cex=0.5)

biplot(pc_catch, cex=0.5)

sumstats_scaled <- sumstats
sumstats_scaled[,-1] <- scale(sumstats_scaled[,-1])

lo_signal <- subset(sumstats, mean<0.05 & sd<0.01)
lo_ids <- lo_signal$reg_id
message(paste("There are",nrow(lo_signal),"cells with very low activity"))

There are 1942 cells with very low activity

Catch-22 variables

Location of low-signal traces in Catch-22 PCA space

plot(pc_catch[['x']][,1], pc_catch[['x']][,2], pch=19, cex=0.5, xlab="PC1", ylab="PC2")
points(pc_catch[['x']][lo_signal$reg_id,1], pc_catch[['x']][lo_signal$reg_id,2], pch=19, cex=0.5, col="yellow")

Boundaries of Catch-22 PCA space

IDs on the boundaries (manually identified from biplot above) 604, 522, 210, 870, 946, 539, 542, 744, 675

eps_val <- 2
clust <- dbscan::dbscan(pc_catch[['x']], eps=eps_val, minPts = 20)

mycols <- rev(dichromat::colorschemes$Categorical.12)[seq(length(unique(clust$cluster)))]

plot(pc_catch[['x']][,1], pc_catch[['x']][,2], pch=19, cex=0.5, 
     col=mycols[clust$cluster+1],
     xlab="PC1", ylab="PC2")
mtext(paste("eps=",eps_val,"produced",length(unique(clust$cluster)),"clusters"), 3)

legend(-12,6,legend=paste("clust",seq(length(unique(clust$cluster)))), col=mycols, pch=19, cex=0.5)

message(cat("With eps=",eps_val,",",length(unique(clust$cluster)), "clusters were identified"))

With eps= 2 , 2 clusters were identified

diprate::nEach(clust$cluster)

   0    1 
1501 4111 

No spikes looks like cluster==1

set.seed(10)
checkthese <- sample(rois[clust$cluster==1], 15)
plotTraceFura(checkthese, d)

Compare to lo_ids

plotTraceFura(lo_ids[1:15], d)

all_no_spike_ids <- union(rois[clust$cluster==1],lo_ids)
length(all_no_spike_ids)

[1] 4211

others (with spikes?) cluster==0

set.seed(6)
checkthese <- sample(rois[clust$cluster==0], 60)
plotTraceFura(checkthese[1:15], d)

Criteria

Philip described a few basic types of traces he sees:

1. no spikes
   
2. periodic spiking (oscillatory)
   
3. sporadic individual spiking (rare and random)
   
4. sporadic sustained spiking
   
5. sustained activation (two-level trace)

Algorithms for classification

Fast fourier transform to detect regular oscillatory patterns

f <- mvfft(as.matrix(frn))

oscil <- sapply(rois, function(roi) any(Re(f[,as.character(roi)])[3:(nrow(f)-3)] > 10))
roi_oscil <- rois[oscil]

plot(seq(2,nrow(f)-1), tail(head(Re(f[,roi_oscil[2]]),-1),-1), type='l', ylim=c(-1,20))

Example oscillatory traces

plotTraceFura(roi_oscil[11:20], d)

plot(pc_catch[['x']][,1], pc_catch[['x']][,2], pch=19, cex=0.5, 
     col=c("blue","orange")[(sumstats$reg_id %in% roi_oscil)+1],
     xlab="PC1", ylab="PC2")

Example traces with hi mean values

hi <- sumstats_scaled[sumstats_scaled$mean >=0.1 & sumstats_scaled$sd <= 0,"reg_id"]

plotTraceFura(hi[1:10], d)

Single spikes

single_spike <- sumstats[sumstats$num.spikes==1,"reg_id"]
mycols3 <- viridisLite::viridis(n=38)

plotTraceFura(single_spike[1:20], d)

hi_slope <- rois[which(l>4e-5)]
plotTraceFura(hi_slope[1:10], d)

plot(pc_catch[['x']][,1], pc_catch[['x']][,2], pch=19, cex=0.5, 
     col=c("blue","orange")[(sumstats$reg_id %in% hi_slope)+1],
     xlab="PC1", ylab="PC2")

plot(pc_catch[['x']][,1], pc_catch[['x']][,2], pch=19, cex=0.5, xlab="PC1", ylab="PC2")
points(pc_catch[['x']][single_spike,1], pc_catch[['x']][single_spike,2], pch=19, cex=0.5, col="yellow")

Expt conditions

plot(pc_catch[['x']][,1], pc_catch[['x']][,2], pch=".", cex=0.5, 
     col=rev(dichromat::colorschemes$Categorical.12)[match(gsub("_[0-9]{1,3}$", "", sumstats$reg_id),conditions)],
     xlab="PC1", ylab="PC2")
legend(-13,8, conditions, col=dichromat::colorschemes$Categorical.12[seq_along(conditions)], cex=0.5, pch=19)

set.seed(13)
my_samples <- invisible(lapply(conditions, function(cond) {
    a <- sampleROIs(condition=cond)
    plotPCcatch(a, clust$cluster, main=cond)
    a
}))

set.seed(13)
my_samples <- invisible(lapply(conditions, function(cond) {
    a <- sampleROIs(condition=cond)
    plotPCcatch(a, as.integer(!rois %in% all_no_spike_ids), main=cond)
    a
}))

lapply(seq_along(my_samples), function(i) plotTraceFura(my_samples[[i]][!my_samples[[i]] %in% all_no_spike_ids][1:20], d))

[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

[[6]]

[[7]]

[[8]]

Example traces

Figure 1B

exemplar_ids <- c(  "20211114_14day8uMPLX_105",
                    "20211114_14day8uMPLX_802",
                    "20211114_14day8uMPLX_297",
                    "20220321_7day8uMPLX_340",
                    "20220321_7day8uMPLX_1121",
                    "20211114_14day8uMPLX_604",
                    "20211114_14day8uMPLX_20"
)

spike_examples <- d[d$reg_id %in% exemplar_ids, ]
spike_examples$reg_id <- factor(spike_examples$reg_id, levels=exemplar_ids)

plotTraceFura(exemplar_ids, spike_examples, yl=c(0.5,1.25))

# ggsave("example_fura2_traces.png", device="png", width=5, height=3.5, units="in")

Plot % in each cluster for each condition

Perform bootstrapping to get sample statistics. Assessing proportions of cells in clusters 0 and 1

# filtered objects in naive data (should filter all datasets) with area > 100 µM^2 

set.seed(13)

prop_spiking <- do.call(c, lapply(conditions, function(co) {
    samp <- lapply(1:100, function(i) sample(rois[grepl(co,rois)],200, replace=TRUE))
    sapply(samp, function(x) length(which(!x %in% all_no_spike_ids))/200)
}))

prop_spiking_df <- data.frame(condition=rep(conditions, each=100), 
                              prop_spiking=prop_spiking)

prop_spiking_cond_df <- prop_spiking_df
prop_spiking_cond_df$date <-  sapply(strsplit(prop_spiking_cond_df$condition, "_"), "[[", 1)
prop_spiking_cond_df$condition <-  sapply(strsplit(prop_spiking_cond_df$condition, "_"), "[[", 2)
prop_spiking_cond_df$condition <-  gsub("day8uMPLX","",prop_spiking_cond_df$condition)
prop_spiking_cond_df[grep("naive",prop_spiking_cond_df$condition),"condition"] <-  0
prop_spiking_cond_df$condition <-  factor(prop_spiking_cond_df$condition, levels=c("0", "5","7","10","14"))
prop_spiking_cond_df <- prop_spiking_cond_df[prop_spiking_cond_df$date != "20230426",]

Time dependence of spiking behavior

Figure 1C

p <- ggplot(data=prop_spiking_cond_df, aes(x=condition, y=prop_spiking, fill=1)) + 
    geom_violin(width=.75) + geom_boxplot(width=0.1, color="grey", alpha=0.5) + 
    # coord_flip() + 
    theme(legend.position="none", 
          axis.text.x = element_text(size=16),
          axis.text.y = element_text(size=16),
          axis.title.x = element_text(size=18, face="bold"),
          axis.title.y = element_text(size=18, face="bold")) + 
    ylim(c(0,0.75)) +
    ylab("Proportion spiking") + 
    xlab("Days in BRAFi")
p

# ggsave("BRAFi_time_prop_spiking.png", device="png", width=4, height=4, units="in")

m <- lm(prop_spiking ~ condition, data=prop_spiking_cond_df)
a <- aov(m)
tukey <- TukeyHSD(a)
tukey$condition

          diff          lwr        upr        p adj
5-0   0.075075  0.065782529 0.08436747 4.747650e-10
7-0   0.204875  0.195582529 0.21416747 4.747650e-10
10-0  0.209325  0.200032529 0.21861747 4.747650e-10
14-0  0.531025  0.521732529 0.54031747 4.747650e-10
7-5   0.129800  0.119069979 0.14053002 4.747650e-10
10-5  0.134250  0.123519979 0.14498002 4.747650e-10
14-5  0.455950  0.445219979 0.46668002 4.747650e-10
10-7  0.004450 -0.006280021 0.01518002 7.880375e-01
14-7  0.326150  0.315419979 0.33688002 4.747650e-10
14-10 0.321700  0.310969979 0.33243002 4.747650e-10

Number of cells per condition

sapply(conditions, function(co) length(unique(rois[grep(co,rois)])))

      20211114_14day8uMPLX             20230426_naive        20220321_5day8uMPLX        20220321_7day8uMPLX       20220321_10day8uMPLX 
                       882                        224                       1202                       1141                       1152 
20230426_8day8uMPLXCa2free          20220425_naive002          20220425_naive003 
                       266                        383                        362 

Analysis of extracellular calcium on long-term BRAFi-treated cell spiking behavior

Supplementary Figure S4

Analysis of manual quantification of 8 day BRAFi w/o calcium

Performed by Philip. Will append manually assessed no-spike reg_ids to all_no_spike_ids.

noCa_8d_manual <- read.csv("../data/20230426_8dBRAFica2free_sample_ids.csv")
testrois <- c(noCa_8d_manual$reg_id, rois[grepl("20220321_7day8uMPLX",rois)])

set.seed(13)
testconds <- c("20220321_7day8uMPLX","20230426_8day8uMPLXCa2free")

prop_spiking_ca <- do.call(c, lapply(testconds, function(co) {
    samp <- lapply(1:100, function(i) sample(testrois[grepl(co,testrois)], 200, replace = TRUE))
    sapply(samp, function(x) length(which(!x %in% c(all_no_spike_ids,noCa_8d_manual$reg_id[noCa_8d_manual$spikes==0])))/200)
}))

prop_spiking_ca_df <- data.frame(condition=rep(c("Ca2+","no Ca2+"), each=100),
                                 prop_spiking=prop_spiking_ca)

8 days BRAFi w/o Ca2+ in buffer vs 7 days BRAFi w/ Ca2+ in buffer

Supplementary Figure S4

p <- ggplot(data=prop_spiking_ca_df, aes(x=condition, y=prop_spiking, fill=1)) + 
    geom_violin(width=.75) + 
    geom_boxplot(width=0.1, color="grey", alpha=0.5) + 
    # coord_flip() +
    theme(legend.position="none", 
          axis.text.x = element_text(size=12),
          axis.text.y = element_text(size=12),
          axis.title.x = element_text(size=16, face="bold"),
          axis.title.y = element_text(size=16, face="bold")) + 
    # ylim(c(0,0.75)) +
    ylab("Proportion spiking") + 
    xlab("") 
p